TY - JOUR
T1 - Five critical quality criteria for artificial intelligence-based prediction models
AU - van Royen, Florien S.
AU - Asselbergs, Folkert W.
AU - Alfonso, Fernando
AU - Vardas, Panos
AU - van Smeden, Maarten
N1 - Funding Information: F.W.A. is supported by UCL Hospitals NIHR Biomedical Research Centre, EU Horizon (AI4HF 101080430 and DataTools4Heart 101057849), and Dutch Research Council (MyDigiTwin 628.011.213). Publisher Copyright: © 2023 The Author(s).
PY - 2023/12/7
Y1 - 2023/12/7
N2 - To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software.
AB - To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software.
KW - Artificial intelligence
KW - Diagnosis
KW - Digital health
KW - Prediction
KW - Prognosis
UR - http://www.scopus.com/inward/record.url?scp=85179139352&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/eurheartj/ehad727
DO - https://doi.org/10.1093/eurheartj/ehad727
M3 - Review article
C2 - 37897346
SN - 0195-668X
VL - 44
SP - 4831
EP - 4834
JO - European Heart journal
JF - European Heart journal
IS - 46
ER -